This is a detailed explanation of Snow Leopard, an open-source project which recognizes player aim patterns based on machine-learning. In this article, you will find the most detailed research result about detecting killaura using Machine Learning in this forum. Maybe after this article is released, everything described below will be cracked and become outdated, but I promise they really work so far. I may never know whether the decision to release this is right, but I'm sure there's a positive impact on it: It can draw a lot more attention to the average developer and it will most likely increase the quality of ML anti cheats we have. Machine Learning Killaura Detection in Minecraft Nascent Nova, 1 February 2018 Abstract Cheating is really an issue on modern Minecraft servers. However, current existing anti-cheat solutions mainly rely on hard-coded checks, which is difficult to maintain and update. This thesis illustrated an alternative way to detect cheaters based on learning vector quantization algorithm in detail, which is more flexible and yields better results. Catalog 1. Introduction 2. Characterization of player’s behavior ....2.1 Hitbox ....2.2 Movement: Head rotation 3. Feature Engineering ....3.1 Collecting data ....3.2 Design of dataset 4. Artificial Neural Network 5. Conclusion 6. Bibliography & Appendix Note: This post may contain redundant content and too formal expression. Note: Some items are expanded or deleted due to the average programming level of the forum. Ps. Don't forget to leave a rating The github link: https://github.com/Nova41/SnowLeopard Spoiler: FAQ & Commands usage Train: /eac train <category-name> Test: /eac test <player-name> <seconds> I prefer 15 seconds Tested on spigot 1.8.8 There is only one permission in the plugin: encanta.ac It is responsible for all sub commands under /eac. Give urself op to bypass the permission ** JAR DOWNLOAD LINK ** https://www.spigotmc.org/resources/snowleopard.55185/ Download from here only if you do not have a compiler to build the source! This may be not up-to-date.